Speakers
Description
Automation has revolutionised lexicography, introducing the ’post-editing lexicography’ model, where the role of the lexicographer involves refining automatically generated dictionary drafts. Since the launch of ChatGPT in November 2022, numerous papers have explored the potential applications of LLMs in dictionary production. The rapid evolution of LLMs necessitates a re-evaluation of conclusions drawn approximately two years prior regarding their application in automating dictionary entry creation, particularly in light of the advanced capabilities demonstrated by contemporary models.
We will illustrate an experiment conducted on a dataset of 400 (397) MWEs with idiomatic meaning, aiming to evaluate the usefulness of LLMs in Serbian descriptive lexicography tasks (idiom generation, word-sense disambiguation of MWEs, definition writing, and generation of illustrative examples). We requested two types of illustrative examples: those in which a MWE has an idiomatic meaning, and examples with that meaning paraphrased literally (without the idiom). We will highlight the challenges and issues encountered with several models (ChatGPT-4o and 4.1, Gemini-2.5-Flash and 2.5-Pro) and discuss the differences in their performance based on given LLM prompts using direct chat and APIs access via Python scripts.
